The present invention relates to background element switching for models in model predictive estimation or control applications and particularly to allowing flexibility in changing the individual models of the model predictive estimation or control application online while maintaining the future prediction of the process as opposed to taking the application offline to make a model change. For example, individual models may be changed in the control or estimation layer of the model. The present invention may be used in a petrochemical process, for example, the process of liquefying a gaseous, methane-rich feed to obtain a liquefied product. The liquefied product is commonly called liquefied natural gas. In particular the present invention relates to using model predictive controllers for controlling the liquefaction process.
The liquefaction process includes the steps of:                (a) supplying the gaseous, methane-rich feed at elevated pressure to a first tube side of a main heat exchanger at its warm end, cooling, liquefying and sub-cooling the gaseous, methane-rich feed against evaporating refrigerant to get a liquefied stream, removing the liquefied stream from the main heat exchanger at its cold end and passing the liquefied stream to storage as liquefied product;        (b) removing evaporated refrigerant from the shell side of the main heat exchanger at its warm end;        (c) compressing in at least one refrigerant compressor the evaporated refrigerant to get high-pressure refrigerant;        (d) partly condensing the high-pressure refrigerant and separating in a separator the partly-condensed refrigerant into a liquid heavy refrigerant fraction and a gaseous light refrigerant fraction;        (e) sub-cooling the heavy refrigerant fraction in a second tube side of the main heat exchanger to get a sub-cooled heavy refrigerant stream, introducing the heavy refrigerant stream at reduced pressure into the shell side of the main heat exchanger at its mid-point, and allowing the heavy refrigerant stream to evaporate in the shell side; and        (f) cooling, liquefying and sub-cooling at least part of the light refrigerant fraction in a third tube side of the main heat exchanger to get a sub-cooled light refrigerant stream, introducing the light refrigerant stream at reduced pressure into the shell side of the main heat exchanger at its cold end, and allowing the light refrigerant stream to evaporate in the shell side.        
International patent application publication No. 99/31448 discloses controlling a liquefaction process by an advanced process controller based on model predictive control to determine simultaneous control actions for a set of manipulated variables in order to optimize at least one of a set of parameters whilst controlling at least one of a set of controlled variables. The set of manipulated variables includes the mass flow rate of the heavy refrigerant fraction, the mass flow rate of the light refrigerant fraction and the mass flow rate of the methane-rich feed. The set of controlled variables includes the temperature difference at the warm end of the main heat exchanger and the temperature difference at the mid-point of the main heat exchanger. The set of variables to be optimized includes the production of liquefied product. The process was considered to be advantageous because the bulk composition of the mixed refrigerant was not manipulated to optimize the production of liquefied product. However, Applicants have now found that the production of the liquefied product is not maximized when the application must be taken offline.
Traditionally model predictive estimators or controllers use linear dynamic models to predict the future of output variables from changes in input variables. These models may be composed of a set of selected single-input/single-output (SISO) relationships. Each of these SISO relationships would be represented by a linear function. However, most processes or systems to which estimators or controllers are applied are nonlinear. The linear model works well over a certain range of operation, but performance can degrade when process conditions change significantly such that the linear model no longer matches the process very well. Traditionally, this problem was addressed by identifying new functions for one or more SISO relationships and then taking the application offline and replacing the SISO relationships with the new functions. Such approach required manual intervention which required costly downtime of the system. When the controller or estimator was re-activated after such a model change, it suffered temporarily from poor performance because of the loss of the future prediction. Also, the process suffered from poor performance while the new relationships were identified and replaced. Thus, the traditional approach was costly and suffered from user errors and poor performance.